A Similarity Mindset Matters on Social Media: Using Algorithm-Generated Similarity Metrics to Foster Assimilation in Upward Social Comparison

Abstract

Upward social comparison on social networking sites (SNSs) makes SNS users feel bad about themselves. Would emphasizing overall similarity between SNS users and the upward comparison targets make them feel good about themselves (i.e., assimilation)? We examined this question using a 3 (overall similarity: zero vs. moderate vs. high) × 2 (comparison dimension: physical appearance vs. financial status) between-subjects online experiment with 143 college students. Participants were recommended with a Facebook user who was physically attractive or financially successful (i.e., an upward comparison target). Right before seeing more details about the target, participants saw a visual cue indicating they shared zero (0%), moderate (50%), or high (90%) overall similarity with the target purported to be algorithm-generated. Results revealed that after seeing images showing the target’s physical attractiveness or financial success, those who shared moderate and high overall similarity with the target rated themselves more positively on physical appearance and financial status and also reported higher liking for the target, the effect of which was mediated by perceived similarity with the target. Moderate, but not high overall similarity improved participants’ life satisfaction. Theoretical and design implications are discussed in light of our findings.